Advanced AI-Powered Universal Control Board with Enhanced Connectivity for HVAC and Refrigeration Systems

The present application includes subject matter disclosed in and claims priority to a provisional application entitled “Advanced AI-Powered Universal Control Board with Enhanced Connectivity for HVAC and Refrigeration Systems” filed on Jan. 26, 2024 and assigned Application No. 63/625,282 describing an invention made by the present inventor.

The present invention generally relates to HVAC systems. More specifically, it relates to a system and method of leveraging AI for HVAC systems.

Existing HVAC systems—such as those in refrigerators, heaters and walk-in freezers often encounter significant challenges that affect their functionality and efficiency. One common problem is inadequate temperature regulation, leading to uneven heating or cooling within an interior space. This issue arises due to various factors such as improper placement of vents, insufficient insulation, or inadequate airflow distribution. For example, in a walk-in freezer used by a grocery store to store perishable goods, certain areas may experience temperature fluctuations, resulting in inconsistent preservation of food items. This inconsistency can lead to product spoilage, affecting the store's bottom line and customer satisfaction. Additionally, temperature stratification can occur within the freezer, where colder air settles near the floor while warmer air accumulates near the ceiling. This stratification can lead to inefficiencies in cooling and increase energy consumption as the cooling system works harder to maintain desired temperatures throughout the entire space. In extreme cases, it can also cause frost buildup on the ceiling or condensation on the floor, creating potential safety hazards and maintenance issues.

Energy inefficiency is another prevalent problem with existing walk-in freezer cooling systems. Many conventional systems operate on fixed schedules or settings that may not account for fluctuations in demand or environmental conditions. For instance, during off-peak hours or cooler weather, the cooling system may continue to run at full capacity, consuming more energy than necessary. This inefficiency not only drives up operating costs but also contributes to carbon emissions and environmental impact. Furthermore, maintenance challenges can arise due to the reactive nature of traditional maintenance practices. HVAC systems require regular inspections, cleaning, and upkeep to ensure optimal performance and longevity. However, without proactive monitoring and predictive maintenance strategies, issues such as compressor failures, refrigerant leaks, or faulty sensors may go unnoticed until they cause significant disruptions to operations. This can result in unexpected downtime, costly repairs, and potential losses of perishable inventory. Overall, these problems with existing walk-in freezer cooling systems underscore the need for innovative solutions to enhance performance, reliability, and efficiency. The refrigeration industry has begun incorporating AI into their systems. United States Patent No. US20210192329A1 disclosed a refrigerator that uses AI to understand door conditions and food status. United States Patent No. US20190392382A1 and Korean Patent No. KR20210093615A teach of refrigerators that leverages AI to understand food stocks and status therein. What is needed is a method to leverage AI to enhance the cooling equipment efficiencies.

The device herein disclosed and described provides a solution to the shortcomings in the prior art through the disclosure of a method of leveraging artificial intelligence (AI) for HVAC systems. An object of the AIPUCB is to enhance the operational efficiencies of HVAC elements. The proactive nature of the system allows it to act on predictions that increase energy efficiency. For example, the AIPUCB examines local climate data and predicts that cooler weather will allow heat to be easily transferred away from a condenser and lower fan speeds thereby saving electrical energy.

Another object of the AIPUCB is to provide a means to control an HVAC system autonomously. When the algorithms make predictions that require corrective actions they can be transmitted to the HVAC and the controller will adjust component functions accordingly. For example, if a temperature is higher than expected in a coolant line, the algorithm predicts temperatures to rise inside a freezer causing spoilage. The AIPUCB sends a command to the controller and the compressor pressures are increased to compress more gas and cool the line to an acceptable range. AI can also be used for predicting whether or not a specific protocol is being followed correctly. For example, if pressure sensors show coolant lines being drained by a technician extremely slowly (rather than quickly as when under a proper vacuum), the AI can notify management that the technician may be performing an illegal operation by releasing coolant into the atmosphere (rather than vacuuming it out properly under high pressure into a recovery unit). In addition, sensors that detect air pressures and flow rates at both the inputs and outputs of a unit allow the AI to predict when air filters, ducts and condenser performance will deteriorate as a result of dirt and suit collected therein.

Another object of the AIPUCB is to apply ‘edge computing’ technologies to existing HVAC systems. A majority of the software operations take place on remote servers on the cloud network rather than locally—thereby saving bandwidth and processing power on the boards at the HVAC units and preventing overloads etc.

Another object of the AIPUCB is to provide ‘over-the-air’ (OtA) firmware updates to multiple HVAC systems automatically. The AIPUCB controller has an onboard, wireless transmitter and memory that can receive such OtA updates remotely and at any time without the need for a site visit.

Another object of the AIPUCB is to provide automated notifications in emergency events. Algorithms have the ability to predict faults that could lead to a catastrophic conditions (such as an ammonia leak etc.). The system sends text, email and SMS messages to the appropriate personnel to correct a condition before it has a chance to become catastrophic. In addition, the AIPUCB also performs self-monitoring to be sure all hardware (sensors etc.) are sound.

Another object of the AIPUCB is to provide a means to maintain operational consistency to prevent costly spoilage of perishables. The system can predict faults before they have an opportunity to bring down a freezer. For example, sensors detect vibrations near a blower motor and send data to the cloud network. Algorithms then predict that a belt will fail soon and a repair person is automatically dispatched for a replacement before the system breaks down and food is wasted. The system automatically generates maintenance tickets and dispatches service personnel. In other embodiments of the AIPUCB, the AI performs price comparisons on various replacement parts and predicts performance durations for each to provide users with a range of choices and prices.

Another object of the AIPUCB is to provide a means to monitor air quality within a conditioned space. For example, the AIPUCB can predict mold growth before it has an opportunity to take hold. Temperature and humidity data are fed to the cloud network and the AI can predict when such growth may occur and automatically reduce humidity using the controller.

Another object of the AIPUCB is to allow users to customize thresholds and benchmarks for all sensor data using the software's mobile app device. These settings can limit the amount of bandwidth used for data transfer. For example, a facility manager sets temperature and humidity readings to only be sent to the cloud network every two hours rather than every minute, unless a specific threshold is triggered—such as when temperatures in a freezer system approach levels higher than a preset degree.

Another object of the AIPUCB is to allow HVAC units to be interconnected to one another. Units that are within close proximity can be connected by ethernet cables or wireless meshnets. For units that have components in various locations on a property, the AIPUCB has longer-range Lorawan capabilities.

Another object of the AIPUCB is to allow users to customize the amount and type of sensors based on their HVAC systems. The controller can accept a wide array of IoT sensors.

Another object of the AIPUCB is to provide a software interface to manage multiple HVACs within a single building or within buildings at multiple locations. This embodiment of the AIPUCB includes a software interface that has a mapping system. This system has pins to show the various locations. Managers can select a location and remotely override sensor and control adjustments, view logs, and view all operations that have been performed on a system over time.

It is briefly noted that upon a reading this disclosure, those skilled in the art will recognize various means for carrying out these intended features of the invention. As such it is to be understood that other methods, applications and systems adapted to the task may be configured to carry out these features and are therefore considered to be within the scope and intent of the present invention, and are anticipated. With respect to the above description, before explaining at least one preferred embodiment of the herein disclosed invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangement of the components in the following description or illustrated in the drawings. The invention herein described is capable of other embodiments and of being practiced and carried out in various ways which will be obvious to those skilled in the art. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.

As such, those skilled in the art will appreciate that the conception upon which this disclosure is based may readily be utilized as a basis for designing of other structures, methods and systems for carrying out the several purposes of the present disclosed device. It is important, therefore, that the claims be regarded as including such equivalent construction and methodology insofar as they do not depart from the spirit and scope of the present invention.

As used in the claims to describe the various inventive aspects and embodiments, “comprising” means including, but not limited to, whatever follows the word “comprising”. Thus, use of the term “comprising” indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present. By “consisting of” is meant including, and limited to, whatever follows the phrase “consisting of”. Thus, the phrase “consisting of” indicates that the listed elements are required or mandatory, and that no other elements may be present.

By “consisting essentially of” is meant including any elements listed after the phrase, and limited to other elements that do not interfere with or contribute to the activity or action specified in the disclosure for the listed elements. Thus, the phrase “consisting essentially of” indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present depending upon whether or not they affect the activity or action of the listed elements. The objects features, and advantages of the present invention, as well as the advantages thereof over existing prior art, which will become apparent from the description to follow, are accomplished by the improvements described in this specification and hereinafter described in the following detailed description which fully discloses the invention, but should not be considered as placing limitations thereon.

Other aspects of the present invention shall be more readily understood when considered in conjunction with the accompanying drawings, and the following detailed description, neither of which should be considered limiting.

In this description, the directional prepositions of up, upwardly, down, downwardly, front, back, top, upper, bottom, lower, left, right and other such terms refer to the device as it is oriented and appears in the drawings and are used for convenience only; they are not intended to be limiting or to imply that the device has to be used or positioned in any particular orientation. Conventional components of the invention are elements that are well-known in the prior art and will not be discussed in detail for this disclosure.

shows a perspective view of the AIPUCB installed on a conventional, HVAC system. The AIPUCB being comprised of but not limited to: controller; ambient temperature sensor; RPM sensor; ambient humidity sensor; in-line high pressure sensor; in-line low pressure sensor; in-line high temperature sensor; in-line low temperature sensor; current sensor; voltage sensor; door status sensor; internal temperature sensor; main board current sensor; rear door sensor; and vibration sensors. In other embodiments, such as those used with heaters, the system also monitors the ignitor coil operation to ensure functionality.

also showing the AIPUCB's software app notifying stakeholderof a problem on a smart phon18. The figure also showing the AIPUCB controller that combines advanced AI processing, dual connectivity modules, precise motor control, and a comprehensive sensor array to optimize system performance, efficiency, energy saving, and adaptability across various HVAC environments. The controller is comprised of, but is not limited to, the following key components and functionalities: a STM32 main processor that serves as the central processing unit for managing complex tasks and system operations; dual ESP32 Modules for mesh network connectivity (enhancing communication range and reliability between multiple devices); an an ESP32 (for Wi-Fi Access Point mode, web server management, and Bluetooth connectivity); an ethernet port for providing a stable and reliable wired network connection; a real-time clock that ensures accurate timekeeping; an EEPROM and a backup battery for continuous operation during power outages; an ADE9789 IC that manages power consumption and precision in AC load control; a 24V DC and AC power supply (such as lithium ion batteries etc.); a PWM for DC or AC motor control (implemented through the STM32 processor for precise speed and torque control); a variable frequency drive (to control AC motor speed and torque); relays to facilitate operation and management of various external devices; and over-the-air updates (automatic wireless firmware updates); Wi-Fi and Bluetooth connectivity; LoRaWAN module; a mesh network module; OLED display, push buttons, LED indicators, an audio buzzer; RS485 communications port; and a programming port.

showing a representative view of the AIPUCB system operations on the cloud networkthat include but are not limited to: administrative routines(subscriptions, user data, etc.); sensor pairing; sensor threshold configurations (maximum and minimum parameter ranges); AI algorithms and prediction analysis; transaction and data library storage; alerts and notifications(text, email, SMS, etc.). The figure showing controllerconnected to a user by means of the cloud networkwho use terminal devices including but not limited to desktop computers, laptops, tablets, and smart phonesand the like. Other embodiments can include an AIPUCB for Furnaces and Water Heaters. For heating units, sensors include a thermocouple at the pilot and ignition coil, a flow meter on the gas lines, air flow meters, a carbon monoxide detector and VOC detector on the exterior of the units. Data is sent from the sensors to the cloud network and the AI predicts potential faults such as gas leaks, excessive boiler temperatures, failing pilot and ignition coils, faulty valves, when a combustion chamber will become too clogged, a blower motor will fail etc. The AI then sends corrective action information to the controller that performs actions such as but not limited to: shutting down the system, adjusting the gas valve control, pilot controls, duct and louver controls etc.

The AIPUCB controller is connected to a multitude of existing components on conventional HVAC units allowing it to make adjustments and changes to the following equipment in real time including but not limited to: blower fan motors; compressor; thermostatic and expansion valve. Sensors for the AIPUCB include but are not limited to: pressure sensors, temperature, current, voltage, door status, and vibration as well as others. Each of these sensors is examined in detail and require a method whereby they are installed into an exiting HVAC system and existing components in the system are controlled by said AI predictions. Steps of the method are presented inand include the following:

Installing pressure sensors on both the high and low sides of the compressor and measuring pressure levels within as well as the coolant lines. The AI algorithms then analyzing real-time pressure data and predicting anomalies such as clogged filters, refrigerant leaks, or abnormal pressure fluctuations, facilitating predictive maintenance and system optimization.

Installing the temperature sensors and monitoring temperature variations within both the high and low sides of the compressor, coolant lines and condenser in real-time. The AI interpreting temperature data and making predictions regarding optimal heating and cooling cycles, detect overheating or freezing conditions, and predict potential system failures, ensuring optimal performance and efficiency. This algorithm also includes superheat and sub-cool calculations for refrigerant management and leak predictions.

Installing electrical current sensors and measuring electrical current (amperage) and voltage flowing through system components in real-time, such as but not limited to: circuit breakers, capacitors, relays, fans, contactors, thermostat, blower and transformer etc. AI algorithms analyzing current fluctuations to identify irregularities in operation, indicating potential issues such as worn-out bearings or electrical faults, enabling proactive maintenance and preventing unexpected breakdowns. The system makes predictions regarding what components may fail and when such a failure may occur. It also allows the system to adjust Variable Frequency Drive and pulse width modulation settings.

Installing the door sensors and detecting the status of doors within the system (open or closed and for how long). The AI interpreting door status data and predicting how such door patterns will impact energy use in the future allowing managers to take administrative measures to ensure doors are always closed when not in use. The sensors also detecting improper sealing, unauthorized access, or safety breaches to enhance security measures.

Installing vibration sensors (gyroscope and linear accelerometer) and measuring vibrations and movements within the system. The AI algorithms analyzing vibration patterns to predict failures such as misaligned components, worn-out bearings, or imbalanced loads in order to enable proactive maintenance.

Installing the air flow sensors and measuring the flow rates at the intake and at the exhausts. The AI analyzing airflow data and predicting obstructions or blockages in ducts or filters, and predict maintenance needs based on airflow degradation over time. For example, when air flows change near the filters at the intake, the AI also predicting when they should be changed to optimize system performance.

Installing the humidity sensor measures humidity levels at the system. AI utilizing humidity data and predicting when mold may start to grow or condensation issues will arise in the surrounding area. The system then adjusting humidification levels to prevent such growth and maintain occupant comfort.

Installing the geolocation sensor and determining the geographical location of the HVAC system. The AI utilizing geolocation data along with weather forecasts and predicting energy usage and comfort levels in response to changing weather patterns and adjust outputs to enhance efficiency and occupant comfort.

showing a preferred embodiment of the AIPUCB software interface that includes: mapping of internal, groupings, and multiple facility HVAC locations along with their maintenance records, and log books. The software having sensor parameters that can be automatically controlled by the AI or manually over-ridden by a user that include but are not limited to: temperature setpoints; run time; coolant line pressures on high and low sides; compressor pressure; door status; pressure switches; emergency shutoff; defrost timer settings; machine tonnage; line set weight; refrigerant type; WiFi; Lorawan and Mesh settings; as well as others that may be applicable to HVAC systems.

It is additionally noted and anticipated that although the device is shown in its most simple form, various components and aspects of the device may be differently shaped or slightly modified when forming the invention herein. As such those skilled in the art will appreciate the descriptions and depictions set forth in this disclosure or merely meant to portray examples of preferred modes within the overall scope and intent of the invention, and are not to be considered limiting in any manner. While all of the fundamental characteristics and features of the invention have been shown and described herein, with reference to particular embodiments thereof, a latitude of modification, various changes and substitutions are intended in the foregoing disclosure and it will be apparent that in some instances, some features of the invention may be employed without a corresponding use of other features without departing from the scope of the invention as set forth. It should also be understood that various substitutions, modifications, and variations may be made by those skilled in the art without departing from the scope of the invention.